Weather data aggregation’s effects on simulation of cropping systems: a model, production system and crop comparison

Gang Zhao, Holger Hoffmann, Lenny van Bussel, Andreas Enders, Xenia Specka, Carmen Sosa, Jagadeesh Yeluripati, Fulu Tao, Julie Constantin, Edmar Teixeira, Balasz Grosz, Luca Doro, Zhigan Zhao, Claas Nendel, Helene Raynal, Henrik Eckersten, Edwin Haas, Matthias Kuhnert, Elisabet Lewan, Michaela Bach, Kurt-Christian Kersebaum, Pier Paolo Roggero, Reimund Rötter, Daniel Wallach, Gunther Krauss, Stefan Siebert, Thomas Gaiser, Ralf Kiese, Enli Wang, Frank Ewert


Interactions of climate, soil and management practices in cropping systems can be simulated at different scales to provide information for decision making. Low resolution simulation need less effort, but important details could be lost through data aggregation effects (DAEs). This paper aims to provide a general method to assess the DAEs on weather data and the simulation of cropping systems, and further investigate how the DAEs vary with changing crop models, crops, variables and production systems. A 30-year continuous cropping system was simulated for winter wheat and silage maize and potential, water-limited and water-nitrogen-limited production situations. Climate data of 1 km resolution and aggregations to resolutions of 10 to 100 km was used as input for the simulations. The data aggregation narrowed the variation of weather data and DAEs increased with increasingly coarser spatial resolution, causing the loss of hot spots in simulated results. Spatial patterns were similar across different resolutions. Consistent with DAEs on weather data, the DAEs on simulated yield (0 to 1.2 t ha-1 for winter wheat and 0 to 1.7 t ha-1 for silage maize), evapotranspiration (3 to 45 mm yr-1 for winter wheat and 4 to 40 mm yr-1 for silage maize), and water use efficiency (0.02 to 0.25 kg m-3­ for winter wheat and 0.04 to 0.4 kg m-3­ for silage maize), increased with coarser spatial resolution. Thus, if spatial information is needed for local management decisions, higher resolution is needed to adequately capture the spatial heterogeneity or hot spots in the region.

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